
'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_			:	Optional[int] 	=       {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_			:	Optional[int] 	=       ['ConvBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_			:	Tuple 	=       [
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_			:	int 	=       [
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   snake_case_			:	Optional[int] 	=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)













'''simple docstring'''





_UpperCamelCase						:						List[str]  =					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def __UpperCAmelCase   (					A					:			int		)    ->   int:
			UpperCAmelCase_      :       str          =       0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
						number //= 1_0_0_0_0_0

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_UpperCamelCase						:						list[bool | None]  =					[None] * 10_000_000
_UpperCamelCase						:						List[str]  =					True
_UpperCamelCase						:						Tuple  =					False
def __UpperCAmelCase   (					A					:			int		)    ->   bool:

			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			UpperCAmelCase_      :       Dict          =       chain(next_number(A		)		)
			UpperCAmelCase_      :       int          =       number_chain

			while number < 1_0_0_0_0_0_0_0:
						UpperCAmelCase_      :       Optional[Any]          =       number_chain
						number *= 1_0

			return number_chain
def __UpperCAmelCase   (					A					:			int = 1_0_0_0_0_0_0_0		)    ->   int:
			for i in range(1			,       A		):
						if CHAINS[i] is None:
									chain(i + 1		)

			return CHAINS[:number].count(A		)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{solution() = }''')


